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.. _sphx_glr_auto_examples:
===============
Example Gallery
===============
In this section, we present illustrative examples for importing files
serialized with the CSD model, using the `csdmpy` package.
Because the CSD model allows multi-dimensional datasets with multiple dependent
variables, we use a shorthand notation of :math:`d\mathrm{D}\{p\}` to
indicate that a dataset has a :math:`p`-component dependent variable defined
on a :math:`d`-dimensional coordinate grid.
In the case of `correlated datasets`, the number of components in each
dependent variable is given as a list within the curly braces, `i.e.`,
:math:`d\mathrm{D}\{p_0, p_1, p_2, ...\}`.
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.. _sphx_glr_auto_examples_1D_1_examples:
Scalar, 1D{1} datasets
======================
The 1D{1} datasets are one dimensional, :math:`d=1`, with one single-component,
:math:`p=1`, dependent variable. These datasets are the most common, and we,
therefore, provide a few examples from various fields of science.
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_0_gmsl_thumb.png
:alt: Global Mean Sea Level rise dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_0_gmsl.py`
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/auto_examples/1D_1_examples/plot_0_gmsl
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_1_NMR_bloch_thumb.png
:alt: Nuclear Magnetic Resonance (NMR) dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_1_NMR_bloch.py`
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/auto_examples/1D_1_examples/plot_1_NMR_bloch
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_2_EPR_thumb.png
:alt: Electron Paramagnetic Resonance (EPR) dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_2_EPR.py`
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/auto_examples/1D_1_examples/plot_2_EPR
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_3_GS_thumb.png
:alt: Gas Chromatography dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_3_GS.py`
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/auto_examples/1D_1_examples/plot_3_GS
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_4_FTIR_thumb.png
:alt: Fourier Transform Infrared Spectroscopy (FTIR) dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_4_FTIR.py`
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/auto_examples/1D_1_examples/plot_4_FTIR
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_5_UV-vis_thumb.png
:alt: Ultraviolet–visible (UV-vis) dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_5_UV-vis.py`
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/auto_examples/1D_1_examples/plot_5_UV-vis
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.. figure:: /auto_examples/1D_1_examples/images/thumb/sphx_glr_plot_6_Mass_thumb.png
:alt: Mass spectrometry (sparse) dataset
:ref:`sphx_glr_auto_examples_1D_1_examples_plot_6_Mass.py`
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.. toctree::
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/auto_examples/1D_1_examples/plot_6_Mass
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.. _sphx_glr_auto_examples_2D_1_examples:
Scalar, 2D{1} datasets
======================
The 2D{1} datasets are two dimensional, :math:`d=2`, with one
single-component dependent variable, :math:`p=1`. Following are some
2D{1} example datasets from various scientific fields expressed in CSDM
format.
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.. figure:: /auto_examples/2D_1_examples/images/thumb/sphx_glr_plot_0_astronomy_thumb.png
:alt: Astronomy dataset
:ref:`sphx_glr_auto_examples_2D_1_examples_plot_0_astronomy.py`
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.. toctree::
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/auto_examples/2D_1_examples/plot_0_astronomy
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.. figure:: /auto_examples/2D_1_examples/images/thumb/sphx_glr_plot_1_NMR_satrec_thumb.png
:alt: Nuclear Magnetic Resonance (NMR) dataset
:ref:`sphx_glr_auto_examples_2D_1_examples_plot_1_NMR_satrec.py`
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/auto_examples/2D_1_examples/plot_1_NMR_satrec
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.. figure:: /auto_examples/2D_1_examples/images/thumb/sphx_glr_plot_2_TEM_thumb.png
:alt: Transmission Electron Microscopy (TEM) dataset
:ref:`sphx_glr_auto_examples_2D_1_examples_plot_2_TEM.py`
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/auto_examples/2D_1_examples/plot_2_TEM
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.. figure:: /auto_examples/2D_1_examples/images/thumb/sphx_glr_plot_3_labeled_thumb.png
:alt: Labeled Dataset
:ref:`sphx_glr_auto_examples_2D_1_examples_plot_3_labeled.py`
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.. toctree::
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/auto_examples/2D_1_examples/plot_3_labeled
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.. _sphx_glr_auto_examples_vector:
Vector datasets
===============
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.. figure:: /auto_examples/vector/images/thumb/sphx_glr_plot_0_vector_thumb.png
:alt: Vector, 1D{2} dataset
:ref:`sphx_glr_auto_examples_vector_plot_0_vector.py`
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/auto_examples/vector/plot_0_vector
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.. figure:: /auto_examples/vector/images/thumb/sphx_glr_plot_1_vector_thumb.png
:alt: Vector, 2D{2} dataset
:ref:`sphx_glr_auto_examples_vector_plot_1_vector.py`
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/auto_examples/vector/plot_1_vector
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.. _sphx_glr_auto_examples_tensor:
Tensor datasets
===============
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.. figure:: /auto_examples/tensor/images/thumb/sphx_glr_plot_0_3D_diff_tensor_mri_thumb.png
:alt: Diffusion tensor MRI, 3D{6} dataset
:ref:`sphx_glr_auto_examples_tensor_plot_0_3D_diff_tensor_mri.py`
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.. toctree::
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/auto_examples/tensor/plot_0_3D_diff_tensor_mri
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.. _sphx_glr_auto_examples_pixel:
Pixel datasets
==============
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.. figure:: /auto_examples/pixel/images/thumb/sphx_glr_plot_0_image_thumb.png
:alt: Image, 2D{3} datasets
:ref:`sphx_glr_auto_examples_pixel_plot_0_image.py`
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/auto_examples/pixel/plot_0_image
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.. _sphx_glr_auto_examples_correlated_examples:
Correlated datasets
===================
The Core Scientific Dataset Model (CSDM) supports multiple dependent
variables that share the same `d`-dimensional coordinate grid, where
:math:`d>=0`.
We call the dependent variables from these datasets as `correlated datasets`.
Following are a few examples of the correlated dataset.
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.. figure:: /auto_examples/correlated_examples/images/thumb/sphx_glr_plot_0_0D11_dataset_thumb.png
:alt: Scatter, 0D{1,1} dataset
:ref:`sphx_glr_auto_examples_correlated_examples_plot_0_0D11_dataset.py`
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/auto_examples/correlated_examples/plot_0_0D11_dataset
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.. figure:: /auto_examples/correlated_examples/images/thumb/sphx_glr_plot_1_meteorology_thumb.png
:alt: Meteorological, 2D{1,1,2,1,1} dataset
:ref:`sphx_glr_auto_examples_correlated_examples_plot_1_meteorology.py`
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/auto_examples/correlated_examples/plot_1_meteorology
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.. figure:: /auto_examples/correlated_examples/images/thumb/sphx_glr_plot_2_astronomy_thumb.png
:alt: Astronomy, 2D{1,1,1} dataset (Creating image composition)
:ref:`sphx_glr_auto_examples_correlated_examples_plot_2_astronomy.py`
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/auto_examples/correlated_examples/plot_2_astronomy
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.. _sphx_glr_auto_examples_sparse:
Sparse datasets
===============
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.. figure:: /auto_examples/sparse/images/thumb/sphx_glr_plot_0_1D_sparse_thumb.png
:alt: Sparse along one dimension, 2D{1,1} dataset
:ref:`sphx_glr_auto_examples_sparse_plot_0_1D_sparse.py`
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/auto_examples/sparse/plot_0_1D_sparse
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.. figure:: /auto_examples/sparse/images/thumb/sphx_glr_plot_1_2D_sparse_thumb.png
:alt: Sparse along two dimensions, 2D{1,1} dataset
:ref:`sphx_glr_auto_examples_sparse_plot_1_2D_sparse.py`
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/auto_examples/sparse/plot_1_2D_sparse
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.. container:: sphx-glr-footer
:class: sphx-glr-footer-gallery
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download all examples in Python source code: auto_examples_python.zip `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip `
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.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_